Abstract : With the advances in cancer research, more and more cancer patients are cured, and they will not relapse or die due to the cancer. It becomes increasingly important to know a patient's chance of being cured given a set of risk factors of the patient, or the survival rate at a certain time if the patient is not cured. The primary aim of this project is to develop a new and 0exible mixture cure model for estimating the proportion of cured patients, and the survival probability of uncured patients. Current methodologies in this area assume that the treatment is e(R)ective at the early stage of trials, while the new model can allow the treatment to have no e(R)ect at the initial time and a gradual e(R)ect later on for uncured patients. This distinctive feature makes the proposed model an important alternative model for practitioners or researchers involved in cancer epidemiology studies in modeling censored survival data with long term survivors. We will develop a semiparametric estimation method, compare it with existing models and methods, evaluate all models by the simulation study, and apply the model to breast cancer data sets. The study will provide practitioners an important tool in analyzing the cancer data with cured fraction and evaluating the risk e(R)ects. The development of the software in the R environment will enable the possible use of the mixture cure model in the epidemiology cancer study easily. PUBLIC HEALTH RELEVANCE: Narrative With the advances in cancer research, more and more cancer patients are cured. This project will investigate a new model for analyzing the cancer survival data with cured patients. The software development in R environment will enable the proposed method used easily in epidemiology cancer study.